code: def forward(
      self,
      x: torch.Tensor,
      self_attn_mask: torch.Tensor = None,
      self_attn_padding_mask: torch.Tensor = None,
      need_weights: bool = False,
      pos_bias=None,
  ):
      residual = x
  
      if self.layer_norm_first:
          x = self.self_attn_layer_norm(x)
          x, attn, pos_bias = self.self_attn(
              query=x,
              key=x,
              value=x,
              key_padding_mask=self_attn_padding_mask,
              need_weights=False,
              attn_mask=self_attn_mask,
              position_bias=pos_bias,
          )
          x = self.dropout1(x)
          x = residual + x
  
          residual = x
          x = self.final_layer_norm(x)
          if self.activation_name == "glu":
              x = self.fc1(x)
          else:
              x = self.activation_fn(self.fc1(x))
          x = self.dropout2(x)
          x = self.fc2(x)
          x = self.dropout3(x)
          x = residual + x
      else:
          x, attn, pos_bias = self.self_attn(
              query=x,
              key=x,
              value=x,
              key_padding_mask=self_attn_padding_mask,
              need_weights=need_weights,
              attn_mask=self_attn_mask,
              position_bias=pos_bias,
          )
  
          x = self.dropout1(x)
          x = residual * self.deep_norm_alpha + x
  
          x = self.self_attn_layer_norm(x)
  
          residual = x
          if self.activation_name == "glu":
              x = self.fc1(x)
          else:
              x = self.activation_fn(self.fc1(x))
          x = self.dropout2(x)
          x = self.fc2(x)
          x = self.dropout3(x)
          x = residual * self.deep_norm_alpha + x
          x = self.final_layer_norm(x)
  
      return x, attn, pos_bias
  
imports: import math
  import numpy
  from typing import Dict, Optional, Tuple
  import torch
  from torch import Tensor, nn
  import torch.nn.functional
  from torch.nn import LayerNorm, Parameter
  from lavis.models.beats.modules import GradMultiply, SamePad, get_activation_fn, GLU_Linear, quant_noise
label_desc: Use image node_id 2 for calling the TransformerSentenceEncoderLayer obj's underlying member method code with example usage: obj.forward(x, _attn_mask, _attn_padding_mask, need_weights, pos_bias) and returns: x,  attn,  pos_bias